Median Filtered Image Restoration and Anti-Forensics Using Adversarial Networks

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Median filtering is used as an anti-forensic technique to erase processing history of some image manipulations such as JPEG, resampling, etc. Thus, various detectors have been proposed to detect median filtered images. To counter these techniques, several anti-forensic methods have been devised as well. However, restoring the median filtered image is a typical ill-posed problem, and thus it is still difficult to reconstruct the image visually close to the original image. Also, it is further hard to make the restored image have the statistical characteristic of the raw image for the anti-forensic purpose. To solve this problem, we present a median filtering anti-forensic method based on deep convolutional neural networks, which can effectively remove traces from median filtered images. We adopt the framework of generative adversarial networks to generate images that follow the underlying statistics of unaltered images, significantly enhancing forensic undetectability. Through extensive experiments, we demonstrate that our method successfully deceives the existing median filtering forensic techniques.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2018-02
Language
English
Article Type
Article
Keywords

DIGITAL IMAGES; TRACES

Citation

IEEE SIGNAL PROCESSING LETTERS, v.25, no.2, pp.278 - 282

ISSN
1070-9908
DOI
10.1109/LSP.2017.2782363
URI
http://hdl.handle.net/10203/240090
Appears in Collection
CS-Journal Papers(저널논문)
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